Detecting Malicious Instructions on a Virtual Machine Using Profiling

ABSTRACT

A system that includes a trusted measurement machine comprising a profiling tool, a semantics virtual machine profiling engine interface, a semantics virtual machine profiling engine. The profiling tool is configured to receive virtual machine operating characteristics metadata for a guest virtual machine and to communicate the virtual machine operating characteristics metadata to the semantics virtual machine profiling engine using the semantics virtual machine profiling engine interface. The profiling tool is further configured to compare the virtual machine operating characteristics metadata to a target profile comprising known configurations for guest virtual machines, to determine a classification for the guest virtual machine, and to communicate the determined classification to the vault management console. The semantics virtual machine profiling engine is configured to generate the target profile based on the virtual machine operating characteristics metadata and to communicate the target profile to the profiling tool using the semantics virtual machine profiling interface.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims benefit of U.S. Provisional PatentApplication No. 62/258,730 filed Nov. 23, 2015 by Jeffery R. Schilling,et al., and entitled “SYSTEM AND METHOD FOR DETECTING MALICIOUSINSTRUCTIONS ON A VIRTUAL MACHINE,” which is incorporated herein byreference as if reproduced in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to computer security andcomputer threat detection, and more specifically to detecting maliciousinstructions on a virtual machine.

BACKGROUND

Cloud computing and virtual computing systems have opened a new door forhackers and other malicious users to gain control of a computer to useits processing power for their purposes and/or to gain access to anysensitive information contained on the computer. Attacks or intrusionsof the computing system may compromise the integrity of the systemand/or the data stored on the system. The stakes are even higher in acloud environment, where many users share the same physical resources.Users desire cloud and virtual computing systems to operate with thesame level of security as physical computing systems so that they can beconfident that their sensitive information is safe.

SUMMARY

In one embodiment, the disclosure includes a system that includes avault management console and a trusted measurement machine incommunication with the virtual management console. The trustedmeasurement machine comprises a profiling tool, a semantics virtualmachine profiling engine interface, a semantics virtual machineprofiling engine. The profiling tool is implemented by a processor, andis configured to receive virtual machine operating characteristicsmetadata for a guest virtual machine and to communicate the virtualmachine operating characteristics metadata to the semantics virtualmachine profiling engine using the semantics virtual machine profilingengine interface. The profiling tool is further configured to comparethe virtual machine operating characteristics metadata to a targetprofile comprising known configurations for guest virtual machines, todetermine a classification for the guest virtual machine based on thecomparison, and to communicate the determined classification to thevault management console. The semantics virtual machine profiling engineinterface is implemented by the processor, and is configured tocommunicate the virtual machine operating characteristics metadata fromthe profiling tool to the semantics virtual machine profiling engine andto communicate the target profile from the semantics virtual machineprofiling engine to the profiling tool. The semantics virtual machineprofiling engine is implemented by the processor, and is configured togenerate the target profile based on the virtual machine operatingcharacteristics metadata and to communicate the target profile to theprofiling tool using the semantics virtual machine profiling interface.

In another embodiment, the disclosure includes a virtual machineintrusion detection method that includes receiving virtual machineoperating characteristics metadata for a guest virtual machine andcommunicating the virtual machine operating characteristics metadata toa semantics virtual machine profiling engine using a semantics virtualmachine profiling engine interface that is implemented by the processor.The method further includes generating a target profile comprising knownconfigurations for guest virtual machines based on the virtual machineoperating characteristics metadata. The method further includescommunicating the target profile to the profiling tool, comparing thevirtual machine operating characteristics metadata to the targetprofile, determining a classification for the guest virtual machinebased on the comparison, and communicating the determined classificationto a vault management console.

The present embodiment presents several technical advantages totechnical problems. In one embodiment, a detection system is configuredto monitoring a system and its data to prevent and/or detect anintrusion or attack. For example, the detection may be configured tomonitor a system for an intrusion by malicious instructions or anunauthorized user. The detection system may monitor and analyze a guestvirtual machine in a secure region of a virtual network such that thepossibly compromised guest virtual machine is unaware that it is beingmonitored. The detection system comprises a virtual vault machine thatis isolated from guest virtual machines, which allows the virtual vaultmachine to perform analysis outside of a possibly infected environmentand reduces influences from malicious instructions or code. Thedetection system is configured to obtain virtual machine operatingcharacteristics metadata for a guest virtual machine and to analyze thevirtual machine operating characteristics metadata without degrading theperformance of the guest virtual machine. The detection system also maybe configured to analyze multiple guest virtual machines simultaneously.The detection system may also be configured to capture instructionsperformed by malicious code, which can be used to analyze the behaviorof malicious code for determining when a guest virtual machine iscompromised.

Certain embodiments of the present disclosure may include some, all, ornone of these advantages. These advantages and other features will bemore clearly understood from the following detailed description taken inconjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following brief description, taken in connection with theaccompanying drawings and detailed description, wherein like referencenumerals represent like parts.

FIG. 1 is a schematic diagram of an embodiment of a detection system fordetecting malicious instructions on a guest virtual machine;

FIG. 2 is a schematic diagram of an embodiment of a virtual vaultmachine of a detection system;

FIG. 3 is a schematic diagram of an embodiment of a trusted measurementmachine of a detection system;

FIG. 4 is a schematic diagram of an embodiment of a guest virtualmachine of a detection system;

FIG. 5 is a schematic diagram of an embodiment of a central processingunit (CPU) for a guest virtual machine with multiple cores;

FIG. 6 is a flowchart of an embodiment of a method for requestingmeasurements from guest virtual machine;

FIG. 7 is a flowchart of an embodiment of a method for measuring virtualmachine characteristics on a guest virtual machine;

FIG. 8 is a flowchart of an embodiment of a method for analyzing virtualmachine operating characteristics metadata;

FIG. 9 is a flowchart of an embodiment of a method for comparing virtualmachine operating characteristics metadata to known guest virtualmachine configurations;

FIG. 10 is a schematic diagram of an embodiment of a configuration ofvirtual machine measurement points for memory metadata; and

FIG. 11 is a flowchart of an embodiment of a memory metadata analysismethod using virtual machine measurement points.

DETAILED DESCRIPTION

Disclosed herein are various embodiments for providing computersecurity, and, more specifically, to monitoring and detecting maliciousor anomalous activity (e.g. an intrusion or an attack) in a virtualnetwork environment such as a cloud computing environment. Maliciousactivities, such as, an intrusion or an attack by maliciousinstructions, in cloud computing environments presents inherenttechnical problems. Examples of such technical problems include thatmalicious actors may be able to disguise malicious activity as normalsystem activity, for example, if the malicious actor is capable ofobfuscating the hard disk or network activities that can be monitored bythe cloud computing environment. Additionally, malicious actors may becapable of intercepting and altering activity information as it is beingcommunicated for the purposes of malicious activity analysis. Forexample, malicious actors may be able to intercept activity informationand to sanitize the information to hide malicious activity that isoccurring in the cloud computing environment. Malicious activities maycompromise the integrity of a computing system and/or the data stored onthe system.

To address these technical problems, a detection system may beconfigured to monitor and detect anomalous or malicious activity, suchas, an intrusion or attack, in guest virtual machines within a publicregion of a virtual network. Monitoring for anomalous or maliciousactivity may allow the detection system to ensure the data integrity ofa computing system by preventing and/or detecting an intrusion orattack. The detection system may periodically collect information (e.g.,metadata or any other appropriate data) from one or more guest virtualmachines to determine whether any malicious activity is present. Thedetection system employs a hypervisor for a guest virtual machine tocommunicate the instructions for collecting metadata from the guestvirtual machine and to forward the collected metadata to a virtual vaultmachine within a secure region of the virtual network for processing.The hypervisor is configured to employ secure connections between thehypervisor and the virtual vault machine that allow the virtual vaultmachine to be isolated from other regions of the virtual network.

The virtual vault machine may analyze the metadata of the virtual vaultmachine to determine whether any malicious activity is present. Forexample, the virtual vault machine may employ a machine learningalgorithm to process the metadata of the guest virtual machine and todetermine whether any malicious activity is present. Additionally oralternatively, the virtual vault machine may forward at least a portionof the metadata of the guest virtual machine to a trusted measurementmachine for a comparative analysis. For example, the trusted measurementmachine may compare the metadata of the guest virtual machine tometadata for known compromised and/or healthy guest virtual machine todetermine a classification (e.g. healthy or compromised) for the guestvirtual machine. The results of the analysis by the virtual vaultmachine and/or the trusted measurement machine may be reported to a user(e.g. a security administrator) when a guest virtual machine has beencompromised. The results of the analysis by the virtual vault machineand/or the trusted measurement machine may also be stored and used foranalyzing other guest virtual machines.

FIG. 1 is a schematic diagram of an embodiment of a detection system 100for detecting malicious instructions on a guest virtual machine 104.Detection system 100 is configured to measure virtual machine operatingcharacteristics metadata from guest virtual machines 104 usinghypervisor control points 124 and virtual machine measurement points 126and to send the virtual machine operating characteristics metadata tovirtual vault machine 106 for analysis using a secure connection.Virtual vault machine 106 is configured to analyze the virtual machineoperating characteristics metadata to detect anomalous activity on theguest virtual machine 104. Virtual vault machine 106 may use informationfrom known threat aggregator 114 and/or trusted measurement machine 108to determine whether a guest virtual machine 104 is compromised.Detection system 100 may be configured to analyze multiple guest virtualmachines 104 simultaneously.

Detection system 100 comprises hypervisor 102, one or more guest virtualmachines 104, one or more virtual vault machines 106, one or moretrusted measurement machines 108, virtualization manager 110, vaultmanagement console 112, and known threat aggregator 114. Detectionsystem 100 may be configured as shown or in any other suitableconfiguration. In an embodiment, hypervisor 102 and guest virtualmachines 104 are located in a public region of a virtual network andvirtual vault machines 106, trusted measurement machines 108, knownthreat aggregator 114, and vault management console 112 are located in asecure region of the virtual network.

Hypervisor 102, guest virtual machines 104, virtual vault machines 106,trusted measurement machines 108, virtualization manager 110, vaultmanagement machine 112, and known threat aggregator 114 may beimplemented using or executed on one or more physical machines. In anembodiment, a physical machine may comprise a memory, a processor, anetwork interface, and an input/output (I/O) interface. For example, thememory may comprise one or more disks, tape drives, or solid-statedrives, and may be used as an over-flow data storage device, to storeprograms when such programs are selected for execution, and to storeinstructions and data that is read during execution. The memory maycomprise read-only memory (ROM), random-access memory (RAM), ternarycontent-addressable memory (TCAM), dynamic random-access memory (DRAM),and static random-access memory (SRAM).

The processor may be implemented as one or more central processing unit(CPU) chips, logic units, cores (e.g. as a multi-core processor),field-programmable gate arrays (FPGAs), application specific integratedcircuits (ASICs), or digital signal processors (DSPs). The processor isoperably coupled to and in signal communication with the memory, thenetwork interface, and the I/O interface. The processor is configured toreceive and transmit electrical signals among one or more of the memory,the network interface, and the I/O interface. The processor isconfigured to process data and may be implemented in hardware orsoftware. An I/O interface may comprise ports, transmitters, receivers,transceivers, or any other devices for transmitting and receiving dataas would be appreciated by one of ordinary skill in the art upon viewingthis disclosure. The network interface may be configured to enable wiredand/or wireless communications and to communicate data through anetwork, virtual network, system, and/or domain. For example, a networkinterface may comprise or may be integrated with a modem, a switch, arouter, a bridge, a server, or a client.

Hypervisor 102 is operably coupled to and configured to exchange (i.e.transmit and receive) data with guest virtual machines 104, virtualvault machines 106, and virtualization manager 110. Hypervisor 102 isconfigured to exchange data with virtual vault machine 106 using asecure virtual network connection 150, to exchange data with guestvirtual machine 104 using a hypervisor connecting interface 152, and toexchange data with virtualization manager 110 using an appropriateinterface, for instance, a Representational State Transfer ApplicationProgramming Interface (REST API). Secure virtual network connection 150may be configured to isolate virtual vault machine 106 fromclient-facing portions of the virtual network. Secure virtual networkconnection 150 may include one or more virtual switches and/or firewallsconfigured to provide security features for the secure portion of thenetwork. Secure virtual network connection 150 may employ encryptionand/or tunneling to provide a secure connection between hypervisor 102and virtual vault machines 106. For example, the secure virtualconnection 150 may comprise a tunnel connection with one or more virtualswitches. In an embodiment, secure virtual network connection 150 may beor may include a socket connection between hypervisor 102 (e.g.,controller connections 116) and virtual vault machine 106 (e.g.hypervisor device driver 136). Alternatively, any other protectedconnection may be employed between hypervisor 102 and virtual vaultmachine 106.

Secure virtual network connection 150 may also be configured to employpolicy rules. For example, secure virtual network connection 150 may beconfigured to employ policy rules that accept data (e.g. packets) onlyfrom known sources (e.g. controller connections 116) and/or rejects data(e.g. packets) from unknown sources. Hypervisor connecting interface 152is configured to provide a secure virtual connection between hypervisor102 and guest virtual machines 104. Hypervisor connecting interface 152may employ encryption, tunneling, and/or a firewall to reduce the riskof a guest virtual machine 104 propagating malicious instructions, code,files, or data through hypervisor 102 to virtual vault machines 106.Secure virtual network connection 150 and hypervisor connectinginterface 152 provide a path among virtual vault machine 106, hypervisor102, and a guest virtual machine 104 that enables secure one-way datapulls or extractions from the guest virtual machine 104 to the virtualvault machine 106. In an embodiment, packets sent from the hypervisorcontrol points 124 to virtual vault machine 106 may comprise encryptedpayloads without an address header or address information (e.g. a sourceaddress and/or a destination address).

Hypervisor 102 may be configured to create, manage, and connect guestvirtual machines 104 within a virtual network such as a cloud computingenvironment. In an embodiment, hypervisor 102 may be configured tocreate virtual appliances including, but not limited to, virtualswitches, virtual firewalls, and virtual routers. Virtual appliances areconfigured to establish connections between or among components within avirtual network. Such virtual components may, in certain embodiments, beconstructs created by way of software working in concert with hardwarecomponents provided by detection system 100.

Hypervisor 102 is configured to establish connections between thehypervisor 102 and guest virtual machines 104, to request virtualmachine operating characteristics metadata from a guest virtual machine104, and to receive the requested virtual machine operatingcharacteristics metadata from a hypervisor control points 124 of theguest virtual machine 104 in response to the request. The receivedvirtual machine operating characteristics metadata may be forwarded tovirtual vault machine 106 for processing and analysis.

Hypervisor 102 may request virtual machine operating characteristicsmetadata from the guest virtual machine 104 continuously, atpredetermined time intervals, in response to a user request for data, orin response to an event notification. In an embodiment, hypervisor 102is configured to request virtual machine operating characteristicsmetadata in response to a user request from virtualization manager 110.In another embodiment, hypervisor 102 may be configured to requestvirtual machine operating characteristics metadata in response toautomated requests from virtual vault machine 106 using the securevirtual network connection 150. An event notification may comprise, incertain embodiments, a message that notifies hypervisor 102 that atrigger event has occurred. Examples of trigger events include, but arenot limited to, a timer expiring or abnormal behavior in a guest virtualmachine 104. Hypervisor 102 is configured to generate a measurementrequest and send the measurement request to hypervisor control points124 in a guest virtual machine 104. Measurement requests may comprisesinformation that indicates which guest virtual machine 104 and virtualmachine operating characteristics metadata to collect and any additionalinstructions for acquiring the virtual machine operating characteristicsmetadata such as a duration for collecting the metadata.

Hypervisor 102 comprises controller connections 116. Controllerconnections 116 are configured to receive measurement commands fromhypervisor 102 and to send a measurement request to hypervisor controlpoints 124 in response to receiving the measurement command. Themeasurement command may comprise a list of requested virtual machineoperating characteristics metadata, durations for collecting therequested virtual machine operating characteristics metadata, and/or apreferred format for the virtual machine operating characteristicsmetadata for a response to a measurement request. Controller connections116 are also configured to receive virtual machine operatingcharacteristics metadata from hypervisor control points 124 in responseto sending the measurements request, and to forward the virtual machineoperating characteristics metadata to hypervisor 102.

In certain embodiments, guest virtual machines 104 comprise computingresources that are available for cloud computing, cloud storage, orother virtual network operations. Guest virtual machines 104 may providecomputing resources that may not be directly correlated to a physicalmachine. Guest virtual machines 104 may be configured to be controlled,managed, or aided by hypervisor 102 or vault management console 112. Inan embodiment, guest virtual machine 104 and a corresponding hypervisor102 may reside on the same physical machine. In another embodiment,guest virtual machine 104 and a corresponding hypervisor 102 may resideon different physical machines. Any suitable number of guest virtualmachines 104 may reside on the same physical machine.

Guest virtual machines 104 may comprise a user space 118 and a kernelspace 120. Instructions executed in a user space (e.g. user space 118)may have limited permission to system resources while instructionsexecuted in a kernel space (e.g. kernel space 120) are provided withpermissive exposure to system resources. The user space comprisesapplication operating on top of the operating system. The kernel spacecomprises drivers operating at the operating system level. In anembodiment, system calls are used to communicate instructions between auser space and a kernel space. Examples of system resources include, butare not limited to, operating system resources and device drivers.Providing operating system (OS)-isolation between user space 118 andkernel space 120 may provide protection in case kernel space 120 iscompromised by malicious instructions. User space 118 comprises one ormore applications 122. Applications 122 may be client-facingapplications capable of being accessed by a client over a network (e.g.the Internet). Applications 122 are executed in user space 118 toprevent public access to the operating system and system resources.

Kernel space 120 comprises hypervisor control points 124 and virtualmachine measurement points 126. Hypervisor control points 124 andvirtual machine measurement points 126 may be configured to operate inthe kernel space 120 to protect hypervisor control points 124 andvirtual machine measurement points 126 from being compromised bymalicious instructions operating in the user space 120. Operating in theuser space 120 also allows hypervisor control points 124 and virtualmachine measurement points 126 to monitor and collect data from a guestvirtual machine 104 without being detected or known to applications 122operating in the user space 120. Hypervisor control points 124 andvirtual machine measurement points 126 may be software or firmware (e.g.drivers) that is executed in hardware (e.g. one or more processors).Hypervisor control points 124 are executed in kernel space 120 to employvirtual machine measurement points 126 to extract virtual machineoperating characteristics metadata from guest virtual machines 104and/or hypervisor 102. For example, hypervisor control points 124 mayoperate inside the operating system of guest virtual machines 104. In anembodiment, hypervisor control points 124 is configured to beimplemented on or executed by a dedicated or separate core than thecores used for resources for the guest virtual machine 104. Additionalinformation for implementing hypervisor control points 124 on adedicated core is described in FIG. 5. Hypervisor control points 124 areconfigured to monitor system calls from applications 122 between userspace 118 and kernel space 120.

Hypervisor control points 124 are configured to receive virtual machineoperating characteristics metadata gathered by virtual machinemeasurement points 126 and to forward at least a portion of the virtualmachine operating characteristics metadata to controller connections 116of hypervisor 102. Information collected by virtual machine measurementpoints 126 may be used for detecting malicious instructions that areexecuting on guest virtual machine 104. Information collected by virtualmachine measurement points 126 may include, but is not limited to, filenames, file hashes, Internet Protocol (IP) addresses, Uniform ResourceLocators (URLs), executable files, system calls/returns, an operatingsystem environment hash, file encryption algorithms, process trees orlists, memory maps, kernel modules, application program interface (API)hooks, file change or modification time information, metadata, memoryfootprints, stream processing unit (SPU) usage, and network usage. In anembodiment, virtual machine measurement points 126 are softwareinstructions that are executed on guest virtual machine 104 and may beexecuted such that the components and applications 122 of the guestvirtual machine 104 are not interrupted by measurements being taken.Hypervisor control points 124 may be configured to be filtered,encrypted, and/or packetize the virtual machine operatingcharacteristics metadata and send the virtual machine operatingcharacteristics metadata to hypervisor 102 using hypervisor connectinginterface 152. Hypervisor control points 124 may be configured toreceive virtual machine operating characteristics metadata continuously,at predetermined time intervals, or in response to a request (e.g.measurement request).

Virtual machine measurement points 126 are configured to monitorphysical and/or virtual hardware of guest virtual machines 104, tomeasure metadata for operating characteristics metadata of the hardware,and to send virtual machine operating characteristics metadata tohypervisor control points 124. Virtual machine measurement points 126may be configured to transform the metadata from machine language to ahuman readable language or format. Virtual machine measurement points126 may extract virtual machine operating characteristics metadata fromuser space 118 and/or kernel space 120 of guest virtual machines 104.Virtual machine measurement points 126 may be dynamically created orremoved from guest virtual machine 104. For example, virtual machinemeasurement points 126 may be created in response to a measurementrequest.

In one embodiment, virtual machine measurement points 126 may beconfigured to perform volatile memory extractions from a guest virtualmachine 104. For example, virtual machine measurement points 126 may beconfigured to scroll through system pages and to extract one or morememory pages or page tables from a memory to be analyzed for maliciousinstructions. The memory page or page table may comprise informationabout currently executing processes or applications.

In another embodiment, virtual machine measurement points 126 may beconfigured to capture data for network traffic introspection. Virtualmachine measurement points 126 may monitor virtualized network interfacecards (VNICs), VXLAN, Vswitches, and/or any other network devices tomonitor inbound and outbound network traffic. Virtual machinemeasurement point 126 may monitor network traffic or activity atdifferent granularity levels. For example, virtual machine measurementpoints 126 may perform fine grain network monitoring via a guest VNIC ormay perform network monitoring on a larger scale via VXLANS andVswitches. Virtual machine measurement points 126 may be able to detectknown threats, for example, based on known IP addresses and ports, andto block traffic for the known threats.

In another embodiment, virtual machine measurement points 126 may beconfigured to capture data from event logging. For example, virtualmachine measurement points 126 may be configured to capture data aboutlog file events or network events.

In another embodiment, virtual machine measurement points 126 may beconfigured to provide file management security. For example, virtualmachine measurement points 126 may be configured to kill or terminateapplications 122, for example, applications 122 with maliciousinstructions, based on known malware hashes. Alternatively, virtualmachine measurement points 126 may be configured to capture any otherdata as would be appreciated by one of ordinary skill in the art uponviewing this disclosure. An example of a guest virtual machine 104 isdescribed in FIG. 4.

Virtual vault machine 106 may be employed to perform threat analysis ofguest virtual machines 104 in a virtual network and may be reliablyclean of any malicious instructions. To help ensure that virtual vaultmachine 106 is free of malicious instructions, virtual vault machine 106may be isolated from portions of the virtual network that are lesssecure against malicious instructions such as guest virtual machines104. In an embodiment, a separate software defined perimeter may becreated and used only by the virtual vault machine 106 to provideisolation from portions of the virtual network that are less secure.Virtual vault machine 106 may be operably coupled to and configured toexchange data with hypervisor 102, trusted measurement machine 108,virtualization manager 110, vault management console 112, and/or knownthreat aggregator 114. Virtual vault machine 106 is configured analyzevirtual machine operating characteristics metadata from guest virtualmachines 104.

In certain embodiments, virtual vault machine 106 may be configured toemploy machine learning algorithms to correlate or compare the behaviorof guest virtual machines 104 to known malicious operating states and toknown healthy operating states for guest virtual machines 104. Virtualvault machine 106 may be configured to pass threat analysis results ofthe virtual machine operating characteristics metadata to vaultmanagement console 112, for example, through a REST API.

A virtual vault machine 106 comprises user space 128 and kernel space130. User space 128 is configured to perform threat analyses andcomprises analysis tool 132 and hypervisor device driver interface 134.Analysis tool 132 and hypervisor device driver interface 134 may besoftware or firmware that is executed in hardware (e.g. one or moreprocessors). Analysis tool 132 may be configured to receive virtualmachine operating characteristics metadata from hypervisor device driverinterface 134 and to employ machine learning algorithms to detectevidence of malicious instructions in the virtual machine operatingcharacteristics. Analysis tool 132 may employ machine learningalgorithms to correlate guest virtual machines 104 with knownconfigurations of other guest virtual machines 104 to classify guestvirtual machines 104 based on the received virtual machine operatingcharacteristics. For example, a machine learning algorithm may classifya guest virtual machine 104 as healthy, compromised, or unknown based onvirtual machine operating characteristics metadata associated with theguest virtual machine 104. Analysis tool 132 may be configured toclassify guest virtual machines 104 by correlating or comparing virtualmachine information to clusters of known healthy virtual machineoperating characteristics metadata and known compromised virtual machinecharacteristics. Analysis tool 132 may also classify guest virtualmachine 104 based on information from known threat aggregator 114, suchas, information about known threats and their system footprints,information from a database (not shown) that stores previouslyclassified virtual machine operating characteristics, and/or semanticanalysis results from trusted measurement machine 108. Analysis tool 132is also configured to send analysis results (e.g. a determinedclassification) to vault management console 112. For example, theanalysis results may comprise at least a portion of the virtual machineoperating characteristics metadata and the determined classification.

In one embodiment, a machine learning algorithm is a clusteringalgorithm or a modified clustering algorithm (e.g. a k-mean clusteringalgorithm) that attempts to correlate or “fit” virtual machine operatingcharacteristics metadata to either a healthy cluster of known guestvirtual machines or a compromised cluster of known guest virtualmachines. Analysis tool 132 may process virtual machine operatingcharacteristics metadata by weighing each characteristic based on itsrelative importance in the detection of malicious instructions or of ahealthy operating state to generate weighted virtual machine operatingcharacteristics.

As an example, a correlation between cursor movements and instructionsfrom a mouse driver may be weighted more strongly for detectingmalicious instructions than a correlation between text input into aninput box and instructions from a keyboard driver, which may beindicative of a copy-paste operation rather than malicious instructions.Analysis tool 132 may classify the weighted virtual machine operatingcharacteristics metadata into a cluster. Analysis tool 132 may classifyweighted virtual machine operating characteristics metadata as unknownwhen a classification threshold is not met. A classification thresholdmay be a predetermined threshold or a dynamically determined threshold.For example, analysis tool 132 may determine the classificationthreshold based on information from a database and known threataggregator 114. As another example, analysis tool 132 may determine theclassification threshold based on information from a database, trustedmeasurement machine 108, known threat aggregator 114, and a thresholdsensitivity parameter determined by and/or received from vaultmanagement console 112.

Hypervisor device driver interface 134 may be configured to receivevirtual machine operating characteristics metadata from hypervisordevice driver 136 and to provide the virtual machine operatingcharacteristics metadata for analysis tool 132 for analysis. Hypervisordevice driver interface 134 may employ system calls to communicate databetween analysis tool 132 and hypervisor device driver 136.

Kernel space 130 comprises hypervisor device driver 136. Hypervisordevice driver 136 may be software or firmware that is executed inhardware. Hypervisor device driver 136 is configured to communicatevirtual machine operating characteristics metadata between hypervisordevice driver interface 134 and hypervisor 102 using secure virtualnetwork connection 150. Hypervisor device driver 136 may be configuredto decrypt virtual machine operating characteristics metadata and/or tocompile the virtual machine operating characteristics metadata into aform or format suitable for analyzing guest virtual machines 104.Hypervisor device driver 136 may be configured to convert the virtualmachine operating characteristics metadata into a data structure (e.g.an array) or a data file, such as, an extensible markup language (XML)file or JavaScript object notation (JSON) file, based on a requestedformat. The virtual machine operating characteristics metadata may alsobe organized to allow analysis tool 132 to efficiently analyze a guestvirtual machine 104.

Trusted measurement machine 108 may be isolated from the public facingvirtual network including guest virtual machines 104 to help ensure thattrusted measurement machine 108 is free of malicious instructions.Trusted measurement machine 108 may be configured analyze guest virtualmachine 104 behavior and characteristics to detect maliciousinstructions. Trusted measurement machine 108 is configured to receivevirtual machine operating characteristics metadata from virtual vaultmachine 106. In one embodiment, trusted measurement machine 108 isconfigured to receive virtual machine operating characteristics metadatafrom the virtual vault machine 106 via virtual management console 112.In another embodiment, trusted measurement machine 108 is configured toreceive virtual machine operating characteristics metadata directly fromvirtual vault machine 106.

Trusted measurement machine 108 is configured to process the virtualmachine operating characteristics metadata and to create a targetprofile for each guest virtual machines 104 and their respective virtualmachine operating characteristics. A target profile may comprise arepository of measurements which may be used to determine a currentstate of a guest virtual machine 104. The current state (e.g. healthy orcompromised) of a guest virtual machine 104 may be used to in thedetermination of whether a particular guest virtual machine 104 istrusted or untrusted. For example, trusted measurement machine 108 mayuse the current state of a guest virtual machine 106 to determinewhether the virtual machine operating characteristics metadata of theguest virtual machine 104 matches expected measurements obtained by thetrusted measurement machine 108. Comparing virtual machine operatingcharacteristics metadata from a guest virtual machine 104 to expectedmeasurements may allow a trusted measurement machine 108 to detectmalicious instructions that are designed to bypass the threat analysisof virtual vault machines 106 by acting like a known and trustedprocess.

Trusted measurement machine 108 comprises user space 138 and kernelspace 140. User space 138 comprises profiling tool 142 and sematicvirtual machine profiling engine interface 144. Profiling tool 142 andsematic virtual machine profiling engine interface 144 may be softwareor firmware that is executed in hardware (e.g. one or more processors).Profiling tool 142 is configured to compile information associated withvirtual machine operating characteristics metadata from known threataggregator 114, virtual vault machine 106, and/or a database (not shown)for initiating a semantic analysis of data measured from guest virtualmachine 104, to send the compiled information to semantic virtualmachine profiling engine 146 via semantic virtual machine profilingengine interface 144, and to receive a target profile in response tosending the complied information.

Target profiles may comprise known configurations for guest virtualmachines 104. Known configurations may comprise known healthyconfiguration and/or known compromised configurations. A target profilemay aid in the detection of obfuscated malicious instructions. Maliciousinstructions may disguise themselves as legitimate applications 122 on aguest virtual machine 104 when executed. A target profile may compriseapplication execution information to compare with the execution of anapplication 122 in a trusted environment. Examples of applicationexecution information include, but are not limited to, an operatingsystem environment has, file encryption algorithms, file names, processtrees or lists, memory maps, kernel modules, API hooks, file change ormodification time information, metadata, system calls/returns, memoryfootprints, SPU usage, central processing unit (CPU) usage, and networkusage. Profiling tool 142 may be configured to perform a comparativeanalysis using the target profile to determine whether a guest virtualmachine 104 is compromised by obfuscated malicious instructions.Profiling tool 142 may be configured to forward comparison results tovirtual vault machine 106 and/or vault management console 112. In oneembodiment, profiling tool 142 may be configured to store the virtualmachine operating characteristics metadata and/or the determinedclassification, for example, in a memory or database (not shown).

Semantic virtual machine profiling engine interface 144 is configured toreceive virtual machine operating characteristics metadata and targetprofile requests from profiling tool 142. Semantics virtual machineprofiling engine interface 144 is also configured to receive targetprofile requests and information from vault management console 112 andknown threat aggregator 114. For example, the semantics virtual machineprofiling engine interface 144 may be configured to send the targetprofile requests to semantic virtual machine profiling engine 146 in thekernel space 140, and to send target profiles to the profiling tool 142and/or to vault management console 112.

Kernel space 140 comprises semantic virtual machine profiling engine146. Semantic virtual machine profiling engine 146 may be software orfirmware that is executed in hardware (e.g. one or more processors).Semantic virtual machine profiling engine 146 is configured to receivetarget profile requests from semantic virtual machine profilinginterface 144 to conduct measurements and to forward target profiles tosemantic virtual machine profiling engine interface 144. Measurementsconducted by semantic virtual machine profiling engine 146 may producetarget profiles that comprise trusted virtual machine operatingcharacteristics metadata that may be compared to virtual machineoperating characteristics metadata of a potentially compromised guestvirtual machine 104. In some embodiments, a comparison between trustedvirtual machine operating characteristics metadata and untrusted virtualmachine operating characteristics metadata may expose maliciousinstructions that attempt to disguise themselves as known applications122. In one embodiment, the comparison of trusted virtual machineoperating characteristics metadata and untrusted virtual machineoperating characteristics metadata may be conducted by semantic virtualmachine profiling engine 146. In another embodiment, trusted measurementmachine 108 may forward trusted virtual machine operatingcharacteristics metadata to vault management console 112 for comparisonand analysis.

Semantic virtual machine profiling engine 146 is also configured toreceive compiled information from profiling tool 142, to analyze thecompiled information (e.g. virtual machine operating characteristicsmetadata), to create a target profile based on the compiled information,and to send the target profile to profiling tool 142 via semanticvirtual machine interface 144. An example of a trusted measurementmachine 108 is described in FIG. 3.

Virtualization manager 110 is configured to configure various componentsof the detection system 100 such as hypervisor 102 and/or guest virtualmachines 104. For example, virtualization manager 110 may configureguest virtual machines 104 so that hypervisor control points 124 andvirtual machine measurement points 126 are isolated from a compromisedoperating system.

Vault management console 112 is configured to manage virtual vaultmachine 106 and trusted measurement machine 108, to send measurementrequests, and to forward threat analysis information from known threatsaggregator 114 to virtual vault machine 106 and/or to trustedmeasurement machine 108. Vault management console 112 may also beconfigured to send measurement requests for periodic metadatameasurements of virtual machine operating characteristics metadata to betaken at a guest virtual machine 104. A measurement request may comprisea list of requested virtual machine operating characteristics metadata,durations for collecting the requested virtual machine operatingcharacteristics metadata, and/or a preferred format for the virtualmachine operating characteristics. For example, vault management console112 may be configured to send measurement requests to hypervisor 102 viavirtual vault machine 106 or virtualization manager 110. Measurementrequests for a guest virtual machine 104 may be sent in response to atimer expiring, a detection system 100 metric exceeding a threshold, auser request, an analysis of prior measurements that prompt furtherinvestigation, or any other stimulus as would be appreciated by one ofordinary skill in the art upon viewing this disclosure. In anembodiment, vault management console 112 may generate measurementrequests that target specific virtual machine operating characteristicsmetadata and virtual machine measurement points 126 based on knownindicators.

Known threats aggregator 114 is configured to derive and/or to provideknown threat information about known malicious instructions to trustedmeasurement machine 108 and virtual vault machine 106. Known threatsaggregator 114 may be configured to provide known threat information totrusted measurement machine 108 and/or virtual vault machine 106 totrain their respective learning algorithms for analyzing guest virtualmachines 104. For example, known threats aggregator 114 may beconfigured to provide clusters of known healthy configurations and/orknown compromised configurations for guest virtual machines 104. In anembodiment, known threats aggregator 114 may be configured to provideknown threat information in response to receiving a request for knownthreat information from trusted measurement machine 108 and/or virtualvault machine 106. For example, a known threat information request maycomprise a request for known threat information including healthy memoryfootprints and compromised memory footprints for a guest virtual machine104.

In operation, vault management console 112 may transmit a measurementrequest that requests virtual machine operating characteristics metadatafrom a guest virtual machine 104 to hypervisor 102. Hypervisor 102identifies a guest virtual machine 104 based on the request and sendsthe measurement request that comprises a list of requested virtualmachine operating characteristics metadata to hypervisor control points124 in the identified guest virtual machine 104. Hypervisor controlpoints 124 receives the measurement request, selects one or morecorresponding virtual machine measurement points 126 based on therequested virtual machine operating characteristics, and sends at leasta portion of the information from the measurement request to theselected virtual machine measurement points 126.

As an example, the measurement request may request virtual machineoperating characteristics metadata of a memory page that corresponds tovirtual memory allocated to a known and trusted application 122.Application 122 may actually be malicious instructions masquerading asapplication 122. Malicious instructions may be unable to hide thesignature they produce in the virtual memory and the memory page that isretrieved by virtual machine measurement points 126.

The received memory page is sent to hypervisor control points 124comprises the malicious instruction signature. Hypervisor control point124 forwards the memory page that comprises the malicious instructionsignature with other virtual machine operating characteristics metadatato hypervisor 102. In an embodiment, hypervisor control points 124bundles the memory page together with other virtual machine operatingcharacteristics, encrypts the virtual machine operating characteristics,and sends the virtual machine operating characteristics metadata viahypervisor 102. In another embodiment, hypervisor 102 bundles the memorypage together with other virtual machine operating characteristics,encrypts the virtual machine operating characteristics, and sends thevirtual machine operating characteristics metadata using secure virtualnetwork connection 150.

Virtual vault machine 106 receives virtual machine operatingcharacteristics metadata from hypervisor 102, decrypts the virtualmachine operating characteristics, and removes any unnecessary data togenerate filtered virtual machine operating characteristics. In oneembodiment, virtual vault machine 106 sends the filtered virtual machineoperating characteristics metadata that comprises the memory page withthe malicious instructions signature to trusted measurement machine 108for a semantics analysis. Trusted measurement machine 108 may use thefiltered virtual machine operating characteristics metadata to create atarget profile that comprises information about operating states ofapplications 122. Trusted measurement machine 108 requests known threatinformation from known threat aggregator 114 based on the targetprofile. Known threat aggregator 114 may comprise virtual machineoperating characteristics metadata for guest virtual machines 104 thatare known to be healthy and for guest virtual machines 104 that areknown to be compromised. Known threat aggregator 114 sends known threatinformation for known healthy guest virtual machines 104 and knowncompromised guest virtual machines 104 based on the target profile totrusted measurement machine 108. As an example, the information fromknown threat aggregator 114 may comprise memory pages from applications122 on a healthy system and memory pages from applications 122 on acompromised system. Trusted measurement machine 108 may use the knownthreat information to determine whether a guest virtual machine 104 iscompromised.

In an embodiment, trusted measurement machine 108 uses a machinelearning algorithm to find a strong correlation between virtual machineoperating characteristics metadata or the guest virtual machine 104 andthe known threat information from a comprised machine and to determinethat the guest virtual machine 104 is compromised. Trusted measurementmachine 108 sends the results of the semantics analysis to the virtualvault machine 106 with a determination that the guest virtual machine104 that corresponds with the virtual machine operating characteristicsmetadata is compromised.

In another embodiment, virtual vault machine 106 may send the virtualmachine operating characteristics metadata that comprises the memorypage with the malicious instructions signature to known threataggregator 114. Virtual vault machine 106 may analyze the virtualmachine operating characteristics metadata to identify signatures ofmalicious instructions that may differ from the malicious instructionsbeing obfuscating as application 122. Virtual vault machine 106 maydetermine that application 122 is compromised based on known threatinformation from known threat aggregator 114, results from trustedmeasurement machine 108, and/or results from analysis tool 132.

Virtual vault machine 106 sends the virtual machine operatingcharacteristics metadata and the determination that the guest virtualmachine 104 is compromised to vault management console 112 and/or knownthreat aggregator 114. In an embodiment, vault management console 112may notify a security administrator of the compromised guest virtualmachine 104 and/or may take automatic actions to isolate and remedy thecompromised guest virtual machine 104. Known threat aggregator 114 mayadd the virtual machine operating characteristics metadata and thedetermination to the known threat information for future use indetecting malicious instructions.

FIG. 2 is a schematic diagram of an embodiment of a virtual vaultmachine 106 of a detection system 100. Virtual vault machine 106 isconfigured to receive virtual machine operating characteristics metadataat hypervisor device driver 136 from hypervisor 102 using secure virtualnetwork connection 150. Upon receiving virtual machine operatingcharacteristics, hypervisor device driver 136 is configured to filterthe virtual machine operating characteristics metadata for analysis andforwards the filtered virtual machine operating characteristics metadatato hypervisor device driver 134. Hypervisor device driver 134 isconfigured to receive the virtual machine operating characteristicsmetadata and to forward the virtual machine operating characteristicsmetadata to analysis tool 132.

Analysis tool 132 may be configured to receive information from knownthreat aggregator 114 and database 202 and to analyze the virtualmachine operating characteristics metadata to classify a guest virtualmachine 104 that is associated with the virtual machine operatingcharacteristics. Database 202 may be a memory. Although FIG. 2 showsdatabase 202 as being incorporated within virtual vault machine 106,database 202 may be a memory that is external to virtual vault machine106. Analysis tool 132 is configured to forward portions of the virtualmachine operating characteristics metadata to trusted measurementmachine 108 for a semantic analysis. Analysis tool 132 is alsoconfigured to send analysis results (e.g. a classification) to vaultmanagement console 112 following the classification of the guest virtualmachine 104 that is associated with the virtual machine operatingcharacteristics.

FIG. 3 is a schematic diagram of an embodiment of a trusted measurementmachine 108 of a detection system 100. Trusted measurement machine 108is configured to receive a portion of the virtual machine operatingcharacteristics metadata from a virtual vault machine 106 for a semanticanalysis at profiling tool 142. Trusted measurement machine 108 is alsoconfigured to store generate and store target profiles for guest virtualmachines 104. Profiling tool 142 is configured to receive informationfrom virtual vault machine 106, known threat aggregator 114, anddatabase 302. Database 302 may be a memory. Although FIG. 3 showsdatabase 302 as being incorporated within trusted measurement machine108, database 302 may be a memory that is external to trustedmeasurement machine 108. Profiling tool 142 is also configured to sendtarget profile requests to semantic virtual machine profiling engine146, and to receive target profiles from semantic virtual machineprofiling engine 146 via semantic virtual machine profiling engineinterface 144.

FIG. 4 is a schematic diagram of an embodiment of a guest virtualmachine 104 of a detection system 100. Guest virtual machine 104comprises CPU 402, kernel modules 404, RAM 406, storage media 408, andVNIC 410. Guest virtual machine 104 may be configured as shown or in anyother suitable configuration. CPU 402, kernel modules 404, RAM 406,storage media 408, and VNIC 410 may be physical devices or virtualizedcomponents that are configured to behave like physical devices. Forexample, RAM 406 may comprise virtual memory addresses 0x0000 to 0xFFFF,which may correspond to addresses 0x002A0002 to 0x002B0001 in physicalmemory.

Hypervisor control points 124 may be implemented or executed on a corethat is held separate from other cores that are available to guestvirtual machine 104. Isolating hypervisor control points 124 on aseparate core may provide OS-isolation which may increase security forhypervisor control points 124 and may reduce the likelihood ofhypervisor control points 124 being compromised by maliciousinstructions, codes, or files. An example of isolating hypervisorcontrol points 124 on a separate core is described in FIG. 5. Hypervisorcontrol points 124 is configured to receive data from virtual machinemeasurement points 126 that are connected to the CPU 402, kernel modules404, RAM 406, storage media 408, VNIC 410, and/or other devices ofinterest in guest virtual machine 104. Virtual machine measurementpoints 126 are configured to collect virtual machine operatingcharacteristics metadata from their respective components and to forwardthe collected virtual machine operating characteristics metadata tohypervisor control points 124. Hypervisor control points 124 areconfigured to forward the virtual machine operating characteristicsmetadata to hypervisor 102 via hypervisor connecting interface 152.

FIG. 5 is a schematic diagram of an embodiment of a CPU 402 for a guestvirtual machine 104 with multiple cores 402A, 402B, 402C, and 402D. Inan embodiment, CPU 402 may be a virtual CPU and the schematicrepresentation may not directly correlate with the physicalrepresentation on a physical CPU. Although FIG. 5 illustrates CPU 402with four cores 402A-402D (i.e. a quad-core CPU), any suitable number ofcores and/or configuration may be employed as would be appreciated byone of ordinary skill in the art upon viewing this disclosure. In oneembodiment, cores 402A-402D may be virtualized such that they arereserved cores on a physical CPU. In another embodiment, cores 402A-402Dis virtualized such that they may be executed dynamically on one or morecores of a physical CPU based on a virtualization algorithm. Cores402A-402D may be configured with complete separation as shown in FIG. 5or may be configured to share physical and/or virtual resources of CPU402 such as caches, ALUs, and registers.

Guest virtual machines 104 may be able to view and/or control all of theavailable cores (e.g. cores 402A-402D) or a subset of the availablecores of CPU 402. In an embodiment, one or more cores 402A-402D may beOS-isolated from guest virtual machines 104 and dedicated to hypervisorcontrol points 124 and/or virtual machine operating points 126. Forexample, cores 402A-402C may be configured to be available andaccessible to a guest virtual machine 104 and core 402D may beOS-isolated from the guest virtual machine 104 and reserved forhypervisor control points 124 and/or virtual machine operating points126. In an embodiment, a core that is reserved for hypervisor controlpoints 124 may be connected to a different virtual network interfacecard than the guest virtual machine 104, which may allow hypervisorcontrol points 124 to receive network traffic without the guest virtualmachine 104 being aware of it.

Malicious instructions may infect a guest virtual machine 104 and mayobfuscate themselves by identifying themselves to an operating system asa known and trusted application 122. In order to detect these types ofmalicious instructions, hypervisor control points 124 are isolated fromthe operating system by executing on core 402D. Core 402D is not visibleto the operating system on a compromised guest virtual machine 104.Hypervisor control points 124 executing on core 402D may have fullaccess to the system resources and operating data of guest virtualmachine 104.

FIG. 6 is a flowchart of an embodiment of a method 600 for requestingvirtual machine operating characteristics metadata measurements fromguest virtual machine 104. Method 600 may be implemented by hypervisor102 to request and obtain virtual machine operating characteristicsmetadata from a guest virtual machine 104 for analysis by a virtualvault machine 106, for example, to determine the health of the guestvirtual machine 104.

At step 602, hypervisor 102 determines whether a trigger is detectedthat indicates to measure virtual machine operating characteristicsmetadata on a guest virtual machine 104. In an embodiment, hypervisor102 may originate the trigger. For example, hypervisor 102 may originatethe trigger based on satisfied criteria such as time elapsed or aparticular network pattern. In another embodiment, vault managementconsole 112 may originate the trigger and send to the trigger tohypervisor 102 via virtual vault machine 106. Vault management console112 may originate the trigger based on detecting suspicious networkbehavior, analysis results from virtual vault machine 106 or trustedmeasurement machine 108, and/or in response to information from knownthreat aggregator 114. Hypervisor 102 proceeds to step 604 whenhypervisor 102 detects a trigger. Otherwise, hypervisor 102 may continueto wait and monitor for triggers.

At step 604, hypervisor 102 sends a measurement request to hypervisorcontrol points 124 of the guest virtual machine 104 to request virtualmachine operating characteristics metadata from the guest machine 104 inresponse to the trigger using controller connections 116. For example,hypervisor 102 may send a measurement command to controller connections116 to request hypervisor control points 124 to obtain virtual machineoperating characteristics metadata for guest virtual machine 104. Themeasurement command may comprise a list of requested virtual machineoperating characteristics metadata, durations for collecting therequested virtual machine operating characteristics metadata, and/or apreferred format for the virtual machine operating characteristicsmetadata for a response to a measurement request. In an embodiment, themeasurement command may comprise information that indicates toinstantiate (i.e. create) a new virtual machine measurement point 126and instructions for which virtual machine operating characteristicsmetadata the new virtual machine measurement point 126 will collect. Inanother embodiment, the measurement command may comprise a targetprofile that indicates instructions for detecting a specific set ofmalicious instructions. For example, the target profile may indicate anamount of virtual machine operating characteristics metadata to becollected. Controller connections 116 sends a measurement request tohypervisor control points 124 that indicates for the hypervisor controlpoints 124 to initiate the collection of virtual machine operatingcharacteristics metadata in response to receiving the measurementcommand from hypervisor 102. Controller connections 116 may generate themeasurement request based on the information provided in the measurementcommand from hypervisor 102.

At step 606, hypervisor 102 receives virtual machine operatingcharacteristics metadata from hypervisor control points 124 in responseto the measurement request. For example, hypervisor 102 receives thevirtual machine operating characteristics metadata via controllerconnections 116. The received virtual machine operating characteristicsmetadata comprises data that corresponds with the requested virtualmachine operating characteristics metadata. In an embodiment, hypervisorcontrol points 124 may compress virtual machine operatingcharacteristics metadata before sending the virtual machine operatingcharacteristics metadata to reduce the size of the information beingcommunicated across the virtual network. Additionally or alternatively,hypervisor control points 124 may encrypt the virtual machine operatingcharacteristics metadata before sending the virtual machine operatingcharacteristics metadata to reduce the likelihood of maliciousinstructions being able to inspect and/or alter the virtual machineoperating characteristics metadata before they are received bycontroller connection 116. In an embodiment, hypervisor control points124 may encrypt the virtual machine operating characteristics metadataas a payload of a packet without an address header or addressinformation (e.g. a source address and a destination address). Thevirtual machine operating characteristics metadata may be encrypted suchthat controller connections 116 and hypervisor 102 are unable to decryptand inspect the virtual machine operating characteristics metadata.Controller connection 116 forwards the virtual machine operatingcharacteristics metadata to hypervisor 102 in response to receiving thevirtual machine operating characteristics metadata from hypervisorcontrol points 124.

At step 608, hypervisor 102 forwards the virtual machine operatingcharacteristics metadata to virtual vault machine 106 using securevirtual network connection 150. In an embodiment, hypervisor 102 mayfilter or organize the virtual machine operating characteristicsmetadata to correspond with the trigger that initiated the automaticvirtual machine measurement.

FIG. 7 is a flowchart of an embodiment of a method 700 for measuringvirtual machine characteristics metadata on a guest virtual machine 104.Method 700 may be implemented by hypervisor control points 124 andvirtual machine measurement points 126 to measure virtual machineoperating characteristics metadata on a guest virtual machine 104.

At step 702, hypervisor control point 124 receives a measurement requestthat identifies virtual machine operating characteristics metadata fromhypervisor 102 using hypervisor connecting interface 152. Themeasurement request may comprise a list of requested virtual machineoperating characteristics metadata, instructions for obtaining therequested virtual machine operating characteristics metadata, durationsfor collecting the requested virtual machine operating characteristicsmetadata, and/or a preferred format for the virtual machine operatingcharacteristics metadata for a response to the measurement request.

At step 704, hypervisor control point 124 selects one or more virtualmachine measurement points 126 to collect the virtual machine operatingcharacteristics metadata identified in the received measurement request.

At step 706, hypervisor control point 124 queries one or more of theselected virtual machine measurement points 126 to collect the virtualmachine operating characteristics metadata. As an example, hypervisorcontrol points 124 may query a virtual machine measurement point 126from a plurality of selected virtual machine measurement points 126 andinstructs the virtual machine measurement point 126 to measure orcollect the virtual machine operating characteristic metadata. In anembodiment, hypervisor control points 124 may query and instruct aplurality of virtual machine measurement points 126 to simultaneouslymeasure or collect the virtual machine operating characteristicsmetadata when multiple virtual machine operating characteristicsmetadata may be measured simultaneously.

At step 708, hypervisor control point 124 receives virtual machineoperating characteristics metadata from the queried virtual machinemeasurement points 126. In an embodiment, hypervisor control points 124may store the virtual machine operating characteristics metadata, forexample, in a memory or database. Hypervisor control points 124 maystore measurements directly or may store a reference or pointer thatpoints to the measurements. Virtual machine operating characteristicsmetadata may be in any suitable format or data structure as would beappreciated by one of ordinary skill in the art upon viewing thisdisclosure.

At step 710, hypervisor control point 124 determines whether all of theselected virtual machine measurement points 126 have been queried andreturned virtual machine operating characteristics metadata. In otherwords, the hypervisor control point 124 determines whether all of thevirtual machine operating characteristics metadata has been collected.

At step 712, hypervisor control point 124 returns to step 706 inresponse to determining that one or more of the selected virtual machinemeasurement points 126 have not been queried or returned virtual machineoperating characteristics metadata. Otherwise, hypervisor control points124 proceeds to step 712 in response to determining that all of theselected virtual machine measurement points 126 have been queried andreturned virtual machine operating characteristics metadata.

At step 714, hypervisor control point 124 generates a packet thatcomprises at least a portion of the requested virtual machine operatingcharacteristics metadata. For example, hypervisor control point 124 maygenerate a packet by inserting at least a portion of the virtual guestmachine operating characteristics metadata as a payload of the packet.In one embodiment, the packet may not comprise a source address and/or adestination address. In an embodiment, hypervisor control points 124 mayorganize the virtual machine operating characteristics metadata anddiscard any irrelevant virtual machine operating characteristicsmetadata. Hypervisor control points 124 may send the virtual machineoperating characteristics metadata to a virtual network interface cardto be packetized. Hypervisor control points 124 may encrypt the packet.In one embodiment, hypervisor control points 124 may encrypt the packetsuch that hypervisor 102 may be able to decrypt the packet. In anotherexample, hypervisor control points 124 may encrypt the packet such thathypervisor 102 may be unable to decrypt the packet. Hypervisor controlpoints 124 may encrypt the packet based on encryption instructions fromhypervisor device driver 136. Alternatively, the hypervisor controlpoints 124 may communicate the encryption information with thehypervisor device driver 136 to allow the hypervisor device driver 136to decrypt the packet.

At step 716, hypervisor control point 124 sends the packet thatcomprises the virtual machine operating characteristics metadata tohypervisor 102 (e.g. controller connections 116) using hypervisorconnecting interface 152.

FIG. 8 is a flowchart of an embodiment of a method 800 for analyzingvirtual machine operating characteristics metadata. Method 800 may beimplemented by virtual vault machine 106 to analyze virtual machineoperating characteristics metadata of a guest virtual machine 104 formalicious instructions and/or anomalous activity, for example, todetermine the health or state of a guest virtual machine 104.

At step 802, virtual vault machine 106 receives a packet comprisingvirtual machine operating characteristics metadata for a guest virtualmachine 104 at hypervisor device driver 136 from hypervisor 102 viasecure virtual network connection 150. Hypervisor device driver 136 maydecrypt the virtual machine operating characteristics metadata when thevirtual machine operating characteristics metadata are encrypted. In oneembodiment, hypervisor device driver 136 may decrypt the virtual machineoperating characteristics metadata based on encryption information thatthe hypervisor device driver 136 provides to hypervisor control points124 or the hypervisor control points 124 provides to the hypervisordevice driver 136.

At step 804, hypervisor device driver 136 forwards the virtual machineoperating characteristics metadata to analysis tool 132 using hypervisordevice driver interface 134. Forwarding the virtual machine operatingcharacteristics metadata to hypervisor device driver interface 134 maytransform the virtual machine operating characteristics metadata toallow the virtual machine operating characteristics metadata to beavailable in the user space 128 for analysis by analysis tool 132. Forexample, the hypervisor device driver 136 may convert the virtualmachine operating characteristics metadata from a first format to asecond format for the analysis tool 132.

At step 806, analysis tool 132 analyzes the virtual machine operatingcharacteristics metadata using a machine learning algorithm. Analysistool 132 may use information from known threats aggregator 114, trustedmeasurement machine 108, and/or database 202 with the machine learningalgorithm.

At step 808, analysis tool 132 determines whether the virtual machineoperating characteristics metadata correlates with a healthy cluster ofknown guest virtual machines. Analysis tool 132 may proceed to step 810when analysis tool 132 determines that the virtual machine operatingcharacteristics metadata correlates with a healthy cluster of knownguest virtual machines. At step 810, analysis tool 132 classifies theguest virtual machine 104 as health or in a healthy state and proceedsto step 818.

Returning to step 808, analysis tool 132 may proceed to step 812 whenanalysis tool 132 determines that the virtual machine operatingcharacteristics metadata do not correlate with a healthy cluster ofknown guest virtual machines. At step 812, analysis tool 132 determineswhether the virtual machine operating characteristics metadatacorrelates with a compromised cluster of known guest virtual machines.Analysis tool 132 may proceed to step 814 when analysis tool 132determines that the virtual machine operating characteristics metadatacorrelates with a compromised cluster of known guest virtual machines.At step 814, analysis tool 132 classifies the guest virtual machine 104as compromised or in a compromised state and proceeds to step 818.

Returning to step 812, analysis tool 132 may proceed to step 816 whenanalysis tool 132 determines that the virtual machine operatingcharacteristics metadata do not correlate with a compromised cluster ofknown guest virtual machines. At step 816, analysis tool 132 classifiesthe guest virtual machine 104 as unknown or in an unknown state andproceeds to step 818.

Optionally, at step 818, analysis tool 132 may remove any virtualmachine operating characteristics metadata that are not relevant to thedetermined classification.

At step 820, analysis tool 132 generates analysis results by combing thevirtual machine operating characteristics metadata with the determinedclassification. At step 822, analysis tool 132 sends the analysisresults to vault management console 112. Alternatively, analysis tool132 may make the analysis results available to vault management console112, for example, via a REST API, and may only send a portion of theanalysis results in response to an analysis results request from vaultmanagement console 112. In one embodiment, analysis tool 132 maycompress the analysis results prior to sending the analysis resultswhich may reduce the amount of data that is sent across the virtualnetwork.

FIG. 9 is a flowchart of an embodiment of a method 900 for comparingvirtual machine operating characteristics metadata to known guestvirtual machine 104 configurations. Method 900 may be implemented bytrusted measurement machine 108 to perform a comparative analysis onvirtual machine operating characteristics metadata to determine thehealth or state of the a guest virtual machine 104. In one embodiment,analysis tool 132 may forward the virtual machine operatingcharacteristics metadata to trusted measurement machine 108 for acomparative analysis when analysis tool 132 classifies virtual machineoperating characteristics metadata as unknown or is unable to determinethe health of a guest virtual machine 104.

At step 902, trusted measurement machine 108 receives virtual machineoperating characteristics metadata for a guest virtual machine 104 atthe profiling tool 142. Virtual machine operating characteristicsmetadata may be sent to trusted measurement machine 108 by vaultmanagement console 112 or virtual vault machine 106. The virtual machineoperating characteristics metadata may be filtered, for example, suchthat information that is not relevant to a classification has beenremoved, or unfiltered. The virtual machine operating characteristicsmetadata may be organized to match the organization of the data receivedfrom known threat aggregator 114. In one embodiment, profiling tool 142may transform or convert the virtual machine operating characteristicsmetadata into a form or format that is compatible with the data receivedfrom known threat aggregator 114. In an embodiment, trusted measurementmachine 108 may also collect information (e.g. known threat information)from known threat aggregator 114.

At step 904, profiling tool 142 forwards the virtual machine operatingcharacteristics metadata to semantic virtual machine profiling engine146 using semantics virtual machine profiling engine interface 144 togenerate target profiles based on the virtual machine operatingcharacteristics. Profiling tool 142 may forward collected informationand the virtual machine operating characteristics metadata to semanticsvirtual machine profiling engine interface 144. Semantics virtualmachine profiling engine interface 144 forwards the collectedinformation and virtual machine operating characteristics metadata fromprofiling tool 142 in the user space 138 of trusted measurement machine108 to semantic virtual machine profiling engine 146 in the kernel space140 of the trusted measurement machine 108.

At step 906, semantics virtual profiling engine 146 sends targetprofiles that are generated based on the collected information (e.g.known threat information) and/or the virtual machine operatingcharacteristics metadata to profiling tool 142 using semantics virtualmachine profiling engine interface 144. The target profiles may compriseknown healthy guest virtual machine 104 configurations and/or knowncompromised guest virtual machine 104 configurations. In an embodiment,semantics virtual profiling engine 146 may send the target profiles in aform or format similar to the virtual machine operating characteristics.Semantics virtual machine profiling engine interface 144 sends thetarget profile from semantics virtual machine profiling engine 146 inthe kernel space 140 to profiling tool 142 in the user space 138 of thetrusted measurement machine 108.

At step 908, profiling tool 142 compares the virtual machine operatingcharacteristics metadata to the target profiles. For example, profilingtool 142 may compare the virtual machine operating characteristicsmetadata to known healthy configurations and known compromisedconfigurations based on the target profiles and/or information fromknown threat aggregator 114. In an embodiment, profiling tool 142 mayuse known healthy configurations and known compromised configurations ina comparison to determine a classification for the virtual machineoperating characteristics. In another embodiment, profiling tool 142 mayuse a clustering algorithm to determine whether the guest virtualmachine 104 is compromised.

At step 910, profiling tool 142 determines whether the virtual machineoperating characteristics metadata matches a healthy configuration.Profiling tool 132 may proceed to step 912 when profiling tool 142determines that the virtual machine operating characteristics metadatamatches a healthy configuration. At step 912, profiling tool 142classifies the guest virtual machine 104 as healthy or in a healthystate and proceeds to step 920.

Returning to step 910, profiling tool 142 may proceed to step 914 whenprofiling tool 142 determines that the virtual machine operatingcharacteristics metadata does not match a healthy configuration. At step914, profiling tool 142 determines whether the virtual machine operatingcharacteristics metadata matches a compromised configuration. Profilingtool 142 may proceed to step 916 when the virtual machine operatingcharacteristics metadata matches a compromised configuration. At step916, profiling tool 142 classifies the guest virtual machine 104 ascompromised or in a compromised state and proceeds to step 920.

Returning to step 914, profiling tool 142 may proceed to step 918 whenprofiling tool 142 determines that the virtual machine operatingcharacteristics metadata do not match a compromised configuration. Atstep 918, profiling tool 142 classifies the guest virtual machine 104 asunknown or in an unknown state and proceeds to step 920.

At step 920, profiling tool 142 sends the determined classification ofthe guest virtual machine 104 to vault management console 112. In anembodiment, profiling tool 142 may trigger an alarm by sending an alarmmessage to the vault management console 112 when the comparison isinconclusive. Vault management console 112 may notify a security managerthat the guest virtual machine 104 cannot be classified as healthy orcompromised and may further investigation may be necessary.

Optionally, at step 922, profiling tool 142 may store the virtualmachine operating characteristics metadata and/or the determineclassification. For example, trusted measurement machine 108 may storethe virtual machine operating characteristics metadata and the determineclassification when profiling tool 142 is able to classify the guestvirtual machine 104 as healthy or compromised based on the virtualmachine operating characteristics metadata associated with the guestvirtual machine 104. The virtual machine operating characteristicsmetadata and the determine classification may be stored in database 302as a known healthy configuration or known compromised configuration.Storing the virtual machine operating characteristics metadata and/orthe determine classification may allow profiling tool 142 to use thestored virtual machine operating characteristics metadata to assist theclustering algorithm for future determinations of the guest virtualmachine 104.

FIG. 10 is a schematic diagram of an embodiment of a configuration 1000of virtual machine measurement points 126 for memory metadata. In anembodiment, hypervisor control point 124 may employ virtual machinemeasurement points 126 to collect memory metadata in response ameasurement request for virtual machine operating characteristics memorymetadata. In an embodiment, hypervisor control point 124 may employhardware assisted paging using hypervisor 102 to monitor and captureguest page tables 1004. Guest page tables 1004 may provide the abilityto monitor currently executing pages and their corresponding page framenumber mapping RAM address. Hardware assisted paging may enable targetedRAM extraction and registry extraction for file metadata information. Inan embodiment, virtual machine measurement points 126 may be configuredto collect virtual machine operating characteristics memory metadataindependent of the operating system of the guest virtual machine 104.

In an embodiment, guest virtual machine 104 comprises process virtualmemory 1002, guest page tables 1004, and guest physical memory 1006.Guest page tables 1004 are configured to map process virtual memory 1002to guest physical memory 1006. For example, guest page tables 1004 mayprovide a mapping between data in process virtual memory 1002 to thelocation of the corresponding data in guest physical memory 1006.Hypervisor 102 comprises extended page tables 1008. Extended page tables1008 may be configured to map data in the memory of guest virtualmachine 104 to host physical memory 1010 in physical hardware 1012.

Virtual machine measurement points 126 may be configured to collectmemory metadata from guest virtual machine 104, hypervisor 102, and/orphysical hardware 1012. For example, virtual machine measurement points126 may be configured to collect one or more memory pages from guestpage tables 1004 and one or more memory pages from extended page tables1008. In an embodiment, the collected memory metadata may be used todetermine whether a guest virtual machine 104 and/or the memory of aguest virtual machine 104 have been compromised. Alternatively, virtualmachine measurement points 126 may be configured to collect any suitablecombination of memory metadata. An example of determining whether aguest virtual machine 104 has been compromised using memory metadata isdescribed in FIG. 11.

FIG. 11 is a flowchart of an embodiment of a memory metadata analysismethod 1100 using virtual machine measurement points 126. Method 1100may be employed by hypervisor control point 124 using virtual machinemeasurement points 126 to capture memory metadata from a guest virtualmachine 104 and a hypervisor 102 that corresponds with the guest virtualmachine 104 to determine whether the memory metadata from the guestvirtual machine 104 has been compromised by comparing the memorymetadata from the guest virtual machine 104 and the hypervisor 102.Compromised memory metadata from the guest virtual machine 104 mayindicate that the operating system of the guest virtual machine 104 hasbeen compromised and may not be trusted Uncompromised memory metadatafrom the guest virtual machine 104 may indicate that the operatingsystem of the guest virtual machine 104 has not been compromised and maybe trusted.

Hypervisor control points 124 are OS-isolated, however, a compromisedoperating system may affect the trustworthiness of virtual machineoperating characteristics metadata that is extracted via hypervisorcontrol points 124. In order to determine whether an operating system iscompromised, hypervisor control points 124 may extract hypervisor memorymetadata (e.g. hypervisor page tables) for comparison to virtual machinememory metadata (e.g. virtual machine page tables) of a guest virtualmachine 104. Hypervisor control points 124 may extract virtual machinememory metadata and hypervisor memory metadata during an initializationperiod or at predetermined time periods.

At step 1102, hypervisor control point 124 employs virtual machinemeasurement points 126 to collect virtual machine memory metadata from aguest virtual machine 104. The virtual machine memory metadata maycomprise one or more memory pages, one or more memory page tables,information about currently executing memory pages or programs, and/ordata from one or more memory locations in the guest virtual machine 104.In one embodiment, virtual machine measurement points 126 may collectthe virtual machine memory metadata from a random access memory of theguest machine 104.

At step 1104, hypervisor control points 124 employs virtual machinemeasurement point 126 to collect hypervisor memory metadata thatcorresponds with the virtual machine memory metadata from a hypervisor102 that is associated with the guest virtual machine 104. Thehypervisor memory metadata may comprise one or more memory pages, one ormore memory page tables, information about currently executing memorypages or programs, and/or data from one or more memory locations in thehypervisor 102. The hypervisor memory is the same type of metadata asthe virtual machine memory metadata from the guest virtual machine 104.For example, the hypervisor memory metadata comprises a memory page whenthe virtual machine memory metadata comprises a memory page. In oneembodiment, virtual machine measurement points 126 may collect thehypervisor memory metadata from a random access memory of the hypervisor102.

At step 1106, hypervisor control point 124 compares the virtual machinememory metadata to the hypervisor memory metadata to determine whetherthe virtual machine memory metadata and the hypervisor memory metadataare the same. In an embodiment, hypervisor control point 124 may comparethe contents of the virtual machine memory metadata and the hypervisormemory metadata. Hypervisor control point 124 may determine that guestvirtual machine 104 is compromised when the virtual machine memorymetadata does not match the hypervisor memory metadata.

At step 1108, hypervisor control point 124 proceeds to step 1110 whenthe virtual machine memory metadata and the hypervisor memory metadataare the same. Otherwise, hypervisor control point 124 proceeds to step1114 when the virtual machine memory metadata and the hypervisor memorymetadata are the different.

At step 1110, hypervisor control point 124 determines that the virtualmachine memory metadata is not compromised. At step 1112, hypervisorcontrol point 124 sends virtual machine memory metadata to virtual vaultmachine 106 for further analysis.

Returning to step 1108, hypervisor control point 124 proceeds to step1114 when the virtual machine memory metadata and the hypervisor memorymetadata are the different. At step 1114, hypervisor control point 124determines that the virtual machine memory metadata is compromised. Atstep 1116, hypervisor control point 124 triggers an alarm to notify asecurity administrator that the guest virtual machine 104 iscompromised.

While several embodiments have been provided in the present disclosure,it should be understood that the disclosed systems and methods might beembodied in many other specific forms without departing from the spiritor scope of the present disclosure. The present examples are to beconsidered as illustrative and not restrictive, and the intention is notto be limited to the details given herein. For example, the variouselements or components may be combined or integrated in another systemor certain features may be omitted, or not implemented.

In addition, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as coupled or directly coupled orcommunicating with each other may be indirectly coupled or communicatingthrough some interface, device, or intermediate component whetherelectrically, mechanically, or otherwise. Other examples of changes,substitutions, and alterations are ascertainable by one skilled in theart and could be made without departing from the spirit and scopedisclosed herein.

To aid the Patent Office, and any readers of any patent issued on thisapplication in interpreting the claims appended hereto, applicants notethat they do not intend any of the appended claims to invoke 35 U.S.C.§112(f) as it exists on the date of filing hereof unless the words“means for” or “step for” are explicitly used in the particular claim.

1. A system comprising: a vault management console; and a trustedmeasurement machine in communication with the virtual managementconsole, and comprising: a profiling tool implemented by a processor,and configured to: receive virtual machine operating characteristicsmetadata for a guest virtual machine; communicate the virtual machineoperating characteristics metadata to a semantics virtual machineprofiling engine using a semantics virtual machine profiling engineinterface; compare the virtual machine operating characteristicsmetadata to a target profile, wherein the target profile comprises knownconfigurations for guest virtual machines; determine a classificationfor the guest virtual machine based on the comparison; and communicatethe determined classification to the vault management console; thesemantics virtual machine profiling engine interface implemented by theprocessor, and configured to: communicate the virtual machine operatingcharacteristics metadata from the profiling tool to the semanticsvirtual machine profiling engine; and communicate the target profilefrom the semantics virtual machine profiling engine to the profilingtool; and the semantics virtual machine profiling engine implemented bythe processor, and configured to: generate the target profile based onthe virtual machine operating characteristics metadata; and communicatethe target profile to the profiling tool using the semantics virtualmachine profiling interface.
 2. The system of claim 1, wherein: theprofiling tool operates in a user space of the trusted measurementmachine; the semantics virtual machine profiling engine operates in akernel space of the trusted measurement machine; and the semanticsvirtual machine profiling engine is configured to: communicate thevirtual machine operating characteristics metadata from the profilingtool in the user space of the trusted measurement machine to thesemantics virtual machine profiling engine in the kernel space of thetrusted measurement machine; and communicate the target profile from thesemantics virtual machine profiling engine in the kernel space of thetrusted measurement machine to the profiling tool in the user space ofthe trusted measurement machine.
 3. The system of claim 1, wherein: thesemantics virtual machine profiling engine is configured to receiveknown threat information; and generating the target profile is based onthe virtual machine operating characteristics metadata and the knownthreat information.
 4. The system of claim 1, wherein the knownconfigurations for guest virtual machines comprises at least one ofknown healthy configurations for guest virtual machines and knowncompromised configurations for guest virtual machines.
 5. The system ofclaim 1, wherein: the known configurations for guest virtual machinescomprises known compromised configurations for guest virtual machines;and the profiling tool classifies the guest virtual machine ascompromised in response to matching the at a least of portion of thevirtual machine operating characteristics metadata to the knowncompromised configurations for guest virtual machines.
 6. The system ofclaim 1, wherein generating the target profile comprises formatting thetarget profile based on the virtual machine operating characteristicsmetadata.
 7. The system of claim 1, wherein the profiling tool isconfigured to store the virtual machine operating characteristicsmetadata and the determined classification.
 8. An apparatus comprising:a profiling tool implemented by a processor, and configured to: receivevirtual machine operating characteristics metadata for a guest virtualmachine; communicate the virtual machine operating characteristicsmetadata to a semantics virtual machine profiling engine using asemantics virtual machine profiling engine interface; compare thevirtual machine operating characteristics metadata to a target profile,wherein the target profile comprises known configurations for guestvirtual machines; determine a classification for the guest virtualmachine based on the comparison; communicate the determinedclassification to the vault management console; the semantics virtualmachine profiling engine interface implemented by the processor, andconfigured to: communicate the virtual machine operating characteristicsmetadata from the profiling tool to the semantics virtual machineprofiling engine; and communicate the target profile from the semanticsvirtual machine profiling engine to the profiling tool; the semanticsvirtual machine profiling engine implemented by the processor, andconfigured to: generate the target profile based on the virtual machineoperating characteristics metadata; and communicate the target profileto the profiling tool using the semantics virtual machine profilinginterface.
 9. The apparatus of claim 8, wherein: the profiling tooloperates in a user space; the semantics virtual machine profiling engineoperates in a kernel space; and the semantics virtual machine profilingengine interface is configured to: communicate the virtual machineoperating characteristics metadata from the profiling tool in the userspace to the semantics virtual machine profiling engine in the kernelspace; and communicate the target profile from the semantics virtualmachine profiling engine in the kernel space to the profiling tool inthe user space.
 10. The apparatus of claim 8, wherein: the semanticsvirtual machine profiling engine is configured to receive known threatinformation; and generating the target profile is based on the virtualmachine operating characteristics metadata and the known threatinformation.
 11. The apparatus of claim 8, wherein the knownconfigurations for the guest virtual machines comprises at least one ofknown healthy configurations for guest virtual machines and knowncompromised configurations for guest virtual machines.
 12. The apparatusof claim 8, wherein: the known configurations for guest virtual machinescomprises known compromised configurations for guest virtual machines;and the profiling tool classifies the guest virtual machine ascompromised in response to matching the at least a portion of thevirtual machine operating characteristics metadata to the knowncompromised configurations for guest virtual machines.
 13. The apparatusof claim 8, wherein generating the target profile compromised formattingthe target profile based on the virtual machine operatingcharacteristics metadata.
 14. The apparatus of claim 8, wherein theprofiling tool is configured to store the virtual machine operatingcharacteristics metadata and the determined classification.
 15. Avirtual machine intrusion detection method comprising: receiving, at aprofiling tool implemented by a processor, virtual machine operatingcharacteristics metadata for a guest virtual machine; communicating, bythe profiling tool, the virtual machine operating characteristicsmetadata to a semantics virtual machine profiling engine using asemantics virtual machine profiling engine interface implemented by theprocessor; generating, by the semantics virtual machine profiling engineimplemented by the processor, a target profile based on the virtualmachine operating characteristics metadata, wherein the target profilecomprises known configurations for guest virtual machines;communicating, by the semantics virtual machine profiling engine, thetarget profile to the profiling tool; comparing, by the profiling tool,the virtual machine operating characteristics metadata to the targetprofile; determining, by the profiling tool, a classification for theguest virtual machine based on the comparison; and communicating, by theprofiling tool, the determined classification to a vault managementconsole.
 16. The method of claim 15, wherein: communicating the virtualmachine operating characteristics metadata comprises communicating thevirtual machine operating characteristics metadata from the profilingtool operating in a user space to the semantics virtual machineprofiling engine operating in a kernel space; and communicating thetarget profile comprises communicating the target profile from thesemantics virtual machine profiling engine in the kernel space to theprofiling tool in the user space.
 17. The method of claim 15, furthercomprising receiving, by the semantics virtual machine profiling engine,known threats information; and wherein the generating the target profileis based on the virtual machine operating characteristics metadata andthe known threats information.
 18. The method of claim 15, wherein theknown configurations for guest virtual machine comprises at least one ofknown healthy configurations for guest virtual machines and knowncompromised configurations for guest virtual machines.
 19. The method ofclaim 15, wherein: the known configurations for guest virtual machinescomprises known compromised configurations for guest virtual machines;and the profiling tool classifies the guest virtual machine ascompromised in response to matching at least a portion of the virtualmachine operating characteristics metadata to known compromisedconfigurations for guest virtual machines.
 20. The method of claim 15,wherein generating the target profile comprises formatting the targetprofile based on the virtual machine operating characteristics metadata.